Easy and reliable maximum a posteriori Bayesian estimation of pharmacokinetic parameters with the open‐source R package mapbayr

Abstract

Pharmacokinetic (PK) parameter estimation is a critical and complex step in the model‐informed precision dosing (MIPD) approach. The mapbayr package was developed to perform maximum a posteriori Bayesian estimation (MAP‐BE) in R from any population PK model coded in mrgsolve. The performances of mapbayr were assessed using two approaches. First, “test” models with different features were coded, for example, first‐order and zero‐order absorption, lag time, time‐varying covariates, Michaelis–Menten elimination, combined and exponential residual error, parent drug and metabolite, and small or large inter‐individual variability (IIV). A total of 4000 PK profiles (combining single/multiple dosing and rich/sparse sampling) were simulated from each test model, and MAP‐BE of parameters was performed in both mapbayr and NONMEM. Second, a similar procedure was conducted with seven “real” previously published models to compare mapbayr and NONMEM on a PK outcome used in MIPD. For the test models, 98% of mapbayr estimations were identical to those given by NONMEM. Some discordances could be observed when dose‐related parameters were estimated or when models with large IIV were used. The exploration of objective function values suggested that mapbayr might outdo NONMEM in specific cases. For the real models, a concordance close to 100% on PK outcomes was observed. The mapbayr package provides a reliable solution to perform MAP‐BE of PK parameters in R. It also includes functions dedicated to data formatting and reporting and enables the creation of standalone Shiny web applications dedicated to MIPD, whatever the model or the clinical protocol and without additional software other than R.

Application of Machine Learning Classification to Improve the Performance of Vancomycin Therapeutic Drug Monitoring

“…Therefore, nine sets of PK parameters were obtained for each PK model for each patient. The R package mapbayr was used for PK parameter estimation [ 38 ].…”

“…In this method, the objective function values (OFVs) for estimating the PK parameters for each model were processed and used as weights. The OFVs were then calculated using the R package mapbayr for PK parameter estimation [ 38 ]. The OFV was processed to a weight using the following equation: where is the number of the th PK model out of the nine models used for parameter estimation.…”

Free and Open-Source Posologyr Software for Bayesian Dose Individualization: An Extensive Validation on Simulated Data

“…For example, for posologyr::poso_dose_auc with p = 0.5, posologyr will determine the optimal dose so that 50% of the probable profiles reach or exceed the target AUC. [17]. The 35 population pharmacokinetic models (Table 1) were transcribed for posologyr (an example is given in Appendix A): default monocompartmental with linear elimination, bicompartmental, with various absorption models (lag-time, zero order, first order, combination of zero and first order kinetics, bioavailability), with nonlinear Michaelis-Menten elimination associated or not with linear elimination, with time-varying covariates, different residual error models (additive, proportional, mixed, log-additive), with two types of observations (parent-metabolite model), and finally with increasing levels of inter-individual variability (with variances ranging from 0.2 to 2).…”

“…To allow comparability of the results of the performance evaluation of the MAP algorithm of posologyr, the primary endpoints were identical to those proposed by Le Louedec et al [17]. The maximum absolute difference was obtained between posologyr (η ik, PGYR ) and NONMEM (η ik,N M ) for each individual according to the following expressions:…”

Development and Validation of Open-Source R Package HMCtdm for Therapeutic Drug Monitoring

“…In addition, P mean k is the mean of population PK parameter, P EST k is the estimated individual PK parameter, and ω is the interindividual variability of the PK parameters for the k-th parameter of a total of L parameters. The MAP estimation was conducted using the R package mapbayr [36].…”